Atmósfera (Journal)
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Exploring the academic perceptions of climate engineering in developing countries
Get PDFThe idea of climate engineering still remains elusive, particularly in several of those developing countries that are most affected by climate change. This knowledge gap can be addressed by knowing the perception of climate change and then introducing and getting feedback on its modification via climate engineering, from the select group of developing countries. Building upon an earlier attempt to achieve these aims, a new group of three developing countries in the global South (Pakistan, Nigeria, and Kenya) is selected to examine their perspective via a total of more than 1000 responses. Descriptive and inferential results indicate that there are significant differences within the global South on awareness of global warming and climate engineering, as well as on the deployment of sulfate aerosols as a measure to delay the harshest effects of global warming
Fuzzy cognitive maps to explore the repercussions of less precipitation on the water supply service of the Metropolitan Area of Mexico City
No full textCurrently the drinking water supply service system of the Metropolitan Area of Mexico City (MCMA) faces serious problems in its operation, which generate highly negative impacts on the impacts on the environmental, social and provider sectors of this system. Given the presence of climate change, we consider the possible scenario in which a decrease in average annual precipitation occurs in the area. To evaluate its impact on each of the sectors, the fuzzy cognitive map (FCM) associated with each of them was constructed. This framework allowed the study of a system whose available information presented large ranges of uncertainty, in addition to being highly complex. Based on the results obtained, we present a mitigation measure for each sector, in order to provide efficient actions to decisions makers. The mathematical simulation was carried out programming in Python
Regional flow climatology for central Mexico (Querétaro): A first case study
Get PDFA flow climatology was established for the Metropolitan Area of Querétaro (MAQ) in central Mexico, by analyzing four years (2014-2017) of back-trajectories generated using the HYSPLIT model. Two flow regimes were found: one from June until September (rainy regime); the other from December to May (dry regime). October and November were considered transition months. Northeasterly flows were present throughout the year; in contrast, trajectories from the southwest were much less frequent and observed mainly during the dry regime. An analysis of the wind fields from the North American Regional Reanalysis (NARR) database for a longer period of time (1979-2019) suggests that these results are representative of the average conditions of the atmosphere at the study site. Some of the northeasterly trajectories observed originate within a dessert region of the state of Querétaro, where several limestone mines are located. During the dry regime and transition months some clusters originate at the industrial area in Guanajuato, which includes the Salamanca refinery. As air transport of pollutants follow these paths, this analysis could be useful for identifying regional sources that affect the MAQ and possibly increase its air pollution load. In fact, the variability of criteria pollutants concentrations matched the flow regimes described above
Distribution and spatio-temporal variation of temperature and precipitation in Sierra de Otontepec Ecological Reserve, Veracruz, Mexico, through GIS modeling
Get PDFSierra de Otontepec Ecological Reserve is an isolated mountain in the Coastal Plain of the Gulf of Mexico with high scientific relevance. However, it lacks climatological information to support the studies of the ecosystems that have been carried out in recent years. GIS modeling characterized its climatology, and climate variability scenarios were created for the period 1981-2010. From temperature and precipitation data recorded in situ, vertical gradients of both variables were obtained in relation to the orography of the zone. The records were correlated with data from nearby weather stations so that by using a DEM, it was possible to obtain raster layers with a resolution of 15 meters per pixel (MPP) for temperature in the summer and winter seasons; and for precipitation in the rainy and dry seasons; the annual value was also calculated for both variables. The climatic variability detected in the zone indicates a gradual increase in air temperature over time and a spatial variation in the distribution of precipitation. The spatial resolution of the modeling is precisely adjusted to the relief of the mountain, allowing the flora and fauna elements found within each pixel to be analyzed with a good level of detail. This work represents the most viable and effective alternative to estimate temperature and precipitation values in mountain systems lacking climatological stations, demonstrating that beyond providing climate information, which is increasingly necessary for ecosystems, it is possible to model their Spatio-temporal dynamics to understand the complex climate variability of our days
Will Mexico meet its climate commitments?
Get PDFIn its Nationally Determined Contribution (NDC), Mexico is committed to reducing unconditionally 22 and 51% of its emissions of greenhouse gases (GHGs) and black carbon, respectively, by 2030, with an emission peak in 2026. Additional reductions of 36 and 70%, respectively, are proposed conditioned to support from other parties. In this work, the percentage of reduction to reach the emission mitigation targets that Mexico proposed in its NDC is estimated. The results show that in order to meet its unconditional NDC, Mexico should start mitigation in 2020 with a 1.5% reduction rate until 2030 and a 3.3% reduction rate by 2050, to reach an emission peak in 2023. To meet the conditional NDC, a 3.1% emission reduction rate until 2030 should be applied, with peak emission in 2021, and 5.8% from 2030 to 2050. In none of these estimates an emission peak in 2026 matches the NDC mitigation options. Furthermore, none of the emissions reduction pathways estimated in this study fulfills the conditional or unconditional contribution and peaks in 2026 at the same time. Mexico has a long history in international climate policy and is a key emerging economy among the top 15 highest GHG emitters. If Mexico does not achieve its NDC, the international implications, both political and climatic, could put the NDC model at risk if there are more large emitters that do not comply with their contribution
Assessing the skill of gridded satellite and reanalysis precipitation products over in East and Southern Africa
Get PDFValidation of gridded precipitation products (GPP) increases the users’ confidence and highlights possible improvements in the algorithms to handle complex rain-forming processes. We evaluated the skill of three GGPs (CHIRPS-v2, CHELSA, and TerraClimate) in estimating the rain gauge observations and compared the precipitation trends derived from these products across the East and Southern Africa (ESA) region. We used Taylor diagrams and Kling-Gupta Efficiency (KGE) to assess the accuracy. A modified Mann-Kendal test and a Sen’s slope estimator were utilized to determine the trends’ significance and magnitude, respectively. The three GPPs had varied performance over temporal and altitudinal ranges. The skill of the three GPPs, at a monthly scale, was generally high but showed lower performance at elevations over 1500 masl, especially during the October-November-December (OND) season. The three GPPs performed equally well between the 1001 – 1500 masl elevation range. CHELSA-v2.1 was most accurate at 0-500 masl but had the lowest skill in both 501 – 1000 and above 1500 masl elevations, which caused over-estimation of the annual and seasonal precipitation trends over mountainous terrain and large inland water bodies. The quantified precipitation trends revealed high spatial-temporal variability. Generally, the skill and precipitation trends derived from CHIRPS-v2 and TC data showed substantial convergence except in Tanzania. Our results emphasize the importance of validating climate datasets to avoid error propagation in different models and applications. Moreover, we demonstrate that new or higher-resolution precipitation data are not always accurate since an algorithm update can introduce artifacts or biases
Distribution changes of the toxic mushroom Amanita phalloides under climate change scenarios and its potential risk over indigenous communities in Mexico
Get PDFAmanita phalloides is a native European deathly ectomycorrhizal mushroom that was introduced to North America and has been expanding its distribution during the last decades. This species is morphologically similar to wild edible mushrooms and if its distribution expands to Mexico, it could represent a risk in terms of food security for local communities. The aim of this study was to evaluate the potential climatic suitability that exists for A. phalloides in North America and overlay it with the distribution of mycophilic communities in Mexico under a baseline climatic scenario and climate change scenarios. To find climatic suitability we modeled its potential distribution with the algorithm that had the best predictive power after pilot test (MaxEnt) using species presences and eight climatic variables chosen with biological and statistical criteria. We worked with CanESM5 because it is one of the best models to simulate climate in North America and SSP5-8.5 scenario in order to be consistent with the precautionary principle. Our results suggest that even when the species has not yet been registered in Mexico, when using European records to model, this country presents 33.61% of climatic suitability for this species under the baseline scenario, potentially affecting about 70% of indigenous communities which are the main consumers of edible mushrooms. Under climate change scenarios, an increase in climatic suitability is expected in Mexico, while decreases are expected in United States and Canada. When using North American records to model, almost no climatic suitability is found in Mexico; however, the implementation of warning campaigns in Mexico is still needed
Performance evaluation of the WRF model under different physical schemes for air quality purposes in Buenos Aires, Argentina
Get PDFThis work presents the performance evaluation of the Weather Research and Forecasting (WRF) model to estimate surface wind speed and direction, air temperature, and water vapor mixing ratio considering 22 configurations at high spatial resolution (1 km) during one week in winter and one week in spring, in order to determine the best-performing schemes for air quality purposes in the Metropolitan Area of Buenos Aires, Argentina. Results show that the use of urban schemes mostly affects wind speed and temperature. The single-layer urban canopy model (UCM) coupled with the Boulac planetary boundary layer (PBL) scheme exhibits the best results for wind speed. Wind direction and water vapor mixing ratio are more sensitive to the land surface model scheme, with results slightly improving with the Noah-MP land surface model. Wind speed and direction errors are larger when the former is lower. When removing from the analysis wind speed values below 2.6 m s–1 for the winter week and 3.1 m s–1 for the spring week, the root mean square errors for wind direction decreased between 50 and 72% of the original value, depending on the configuration and week. Overall, under the studied conditions, configurations including Noah-Mp land surface model or the combination of a simple UCM with BouLac PBL are suitable for air quality applications, as they reproduce both temperature and water vapor mixing ratio relatively well, with errors below 10% and Correlation values above 0.7, and are the best performing configurations for wind direction and speed, respectively
Nowcasting severity of thunderstorm associated with strong wind flow over Indian Subcontinent: Resource lightning surge
Get PDFThis study uses the data of Indian Air Force (IAF) Lightning Detection System (LDS) network to prepare climatological plots of lightning over India and to formulate location-specific thunderstorm (TS) guidance for a total of 12 Indian airports. The analysis of climatological plots reveals that there is a distinct warm-season preponderance of lightning strikes over Indian subcontinent, with pre-monsoon months receiving the maximum lightning. The most probable time of occurrence being 12:00-14:00 UTC during all the seasons across the country. Location-specific TS guidance not only signifies the most probable direction of occurrence of TS with respect to the airport, but also clearly brings out the favorable direction of movement. Hence, the same can be judiciously used as nowcasting aid. Further, the characteristic features of lightning, like surges in flash rate, can be objectively used to define a predictor for nowcasting severe weather associated with a TS cloud. The study of these surges in lightning flash rate vis a vis the occurrence of strong surface winds (SSW) > 60 km h–1 over Delhi National Capital Region (NCR) (Hindan Airport observations), indicated that there is an increase in the number of lightning flashes prior to the occurrence of SSW. Within 45 min of their occurrence, 77.5% of SSW are preceded by surges in flash rates; however, the probability of detection of the event with a lead time of 15 to 45 min is around 71%
Analyzing and forecasting lightning flashes and the related wind gusts at a wind energy power plant in a hilly region of western Greece
Get PDFWind power plants are vulnerable to abrupt weather changes caused by thunderstorms associated with lightning activity and accompanying severe wind gusts and rapid wind direction changes. Due to the damages that such phenomena may cause, the knowledge of the relationship between storm systems and the produced wind field is essential during the construction and operation phase of a plant. In the first part of this study, the relationship between severe wind gusts and lightning activity in a power plant in Greece is investigated. Wind data are measured at the wind turbines for a 3-year period (2012-2014); the corresponding lightning data come from the ZEUS lighting detection network. Wind gusts are well correlated to lightning strikes. This correlation is maximized during winter when well organized weather systems affect the area and minimized in summer as a result of local storms due to thermal instability. The second part of the study focuses on the development of an artificial neural network (ANN) model in order to forecast these two parameters in a 1-h ahead horizon based on wind speed, wind direction, and maximum observed wind gust measured at the nacelle of a wind turbine and four other variables, namely CAPE, TTI, wind speed at the 500 hPa isobaric level, and the 0-6 km vertical wind shear. The proposed model could be considered as a promising tool in simulating the occurrence both of wind gusts and lightning flashes, providing a relatively good evidence of the possibility of occurrence of such events